In business and beyond, communication is king. Successful service level agreements (SLAs) operate on this principle, laying the foundation for successful provider-customer relationships.
A service level agreement (SLA) is a key component of technology vendor contracts that describes the terms of service between a service provider and a customer. SLAs describe the level of performance to be expected, how performance will be measured and repercussions if levels are not met. SLAs make sure that all stakeholders understand the service agreement and help forge a more seamless working relationship.
Types of SLAs
There are three main types of SLAs:
Customer-level SLAs
Customer-level SLAs define the terms of service between a service provider and a customer. A customer can be external, such as a business purchasing cloud storage from a vendor, or internal, as is the case with an SLA between business and IT teams regarding the development of a product.
Service-level SLAs
Service providers who offer the same service to multiple customers often use service-level SLAs. Service-level SLAs do not change based on the customer, instead outlining a general level of service provided to all customers.
Multilevel SLAs
When a service provider offers a multitiered pricing plan for the same product, they often offer multilevel SLAs to clearly communicate the service offered each level. Multilevel SLAs are also used when creating agreements between more than two more parties.
SLA components
SLAs include an overview of the parties involved, services to be provided, stakeholder role breakdowns, performance monitoring and reporting requirements. Other SLA components include security protocols, redressing agreements, review procedures, termination clauses and more. Crucially, they define how performance will be measured.
SLAs should precisely define the key metrics—service-level agreement metrics—that will be used to measure service performance. These metrics are often related to organizational service level objectives (SLOs). While SLAs define the agreement between organization and customer, SLOs set internal performance targets. Fulfilling SLAs requires monitoring important metrics related to business operations and service provider performance. The key is monitoring the right metrics.
What is a KPI in an SLA?
Metrics are specific measures of an aspect of service performance, such as availability or latency. Key performance indicators (KPIs) are linked to business goals and are used to judge a team’s progress toward those goals. KPIs don’t exist without business targets; they are “indicators” of progress toward a stated goal.
Let’s use annual sales growth as an example, with an organizational goal of 30% growth year-over-year. KPIs such as subscription renewals to date or leads generated provide a real-time snapshot of business progress toward the annual sales growth goal.
Metrics such as application availability and latency help provide context. For example, if the organization is losing customers and not on track to meet the annual goal, an examination of metrics related to customer satisfaction (that is, application availability and latency) might provide some answers as to why customers are leaving.
What SLA metrics to monitor
SLAs contain different terms depending on the vendor, type of service provided, client requirements, compliance standards and more and metrics vary by industry and use case. However, certain SLA performance metrics such as availability, mean time to recovery, response time, error rates and security and compliance measurements are commonly used across services and industries. These metrics set a baseline for operations and the quality of services provided.
Clearly defining which metrics and key performance indicators (KPIs) will be used to measure performance and how this information will be communicated helps IT service management (ITSM) teams identify what data to collect and monitor. With the right data, teams can better maintain SLAs and make sure that customers know exactly what to expect.
Ideally, ITSM teams provide input when SLAs are drafted, in addition to monitoring the metrics related to their fulfillment. Involving ITSM teams early in the process helps make sure that business teams don’t make agreements with customers that are not attainable by IT teams.
SLA metrics that are important for IT and ITSM leaders to monitor include:
1. Availability
Service disruptions, or downtime, are costly, can damage enterprise credibility and can lead to compliance issues. The SLA between an organization and a customer dictates the expected level of service availability or uptime and is an indicator of system functionality.
Availability is often measured in “nines on the way to 100%”: 90%, 99%, 99.9% and so on. Many cloud and SaaS providers aim for an industry standard of “five 9s” or 99.999% uptime.
For certain businesses, even an hour of downtime can mean significant losses. If an e-commerce website experiences an outage during a high traffic time such as Black Friday, or during a large sale, it can damage the company’s reputation and annual revenue. Service disruptions also negatively impact the customer experience. Services that are not consistently available often lead users to search for alternatives. Business needs vary, but the need to provide users with quick and efficient products and services is universal.
Generally, maximum uptime is preferred. However, providers in some industries might find it more cost effective to offer a slightly lower availability rate if it still meets client needs.
2. Mean time to recovery
Mean time to recovery measures the average amount of time that it takes to recover a product during an outage or failure. No system or service is immune from an occasional issue or failure, but enterprises that can quickly recover are more likely to maintain business profitability, meet customer needs and uphold SLAs.
3. Response time and resolution time
SLAs often state the amount of time in which a service provider must respond after an issue is flagged or logged. When an issue is logged or a service request is made, the response time indicates how long it takes for a provider to respond to and address the issue. Resolution time refers to how long it takes for the issue to be resolved. Minimizing these times is key to maintaining service performance.
Organizations should seek to address issues before they become system-wide failures and cause security or compliance issues. Software solutions that offer full-stack observability into business functions can play an important role in maintaining optimized systems and service performance. Many of these platforms use automation and machine learning (ML) tools to automate the process of remediation or identify issues before they arise.
For example, AI-powered intrusion detection systems (IDS) constantly monitor network traffic for malicious activity, violations of security protocols or anomalous data. These systems deploy machine learning algorithms to monitor large data sets and use them to identify anomalous data. Anomalies and intrusions trigger alerts that notify IT teams. Without AI and machine learning, manually monitoring these large data sets would not be possible.
4. Error rates
Error rates measure service failures and the number of times service performance dips below defined standards. Depending on your enterprise, error rates can relate to any number of issues connected to business functions.
For example, in manufacturing, error rates correlate to the number of defects or quality issues on a specific product line, or the total number of errors found during a set time interval. These error rates, or defect rates, help organizations identify the root cause of an error and whether it’s related to the materials used or a broader issue.
There is a subset of customer-based metrics that monitor customer service interactions, which also relate to error rates.
First call resolution rate: In the realm of customer service, issues related to help desk interactions can factor into error rates. The success of customer services interactions can be difficult to gauge. Not every customer fills out a survey or files a complaint if an issue is not resolved—some will just look for another service. One metric that can help measure customer service interactions is the first call resolution rate. This rate reflects whether a user’s issue was resolved during the first interaction with a help desk, chatbot or representative. Every escalation of a customer service query beyond the initial contact means spending on extra resources. It can also impact the customer experience.
Abandonment rate: This rate reflects the frequency in which a customer abandons their inquiry before finding a resolution. Abandonment rate can also add to the overall error rate and helps measure the efficacy of a service desk, chatbot or human workforce.
5. Security and compliance
Large volumes of data and the use of on-premises servers, cloud servers and a growing number of applications creates a greater risk of data breaches and security threats. If not monitored appropriately, security breaches and vulnerabilities can expose service providers to legal and financial repercussions.
For example, the healthcare industry has specific requirements around how to store, transfer and dispose of a patient’s medical data. Failure to meet these compliance standards can result in fines and indemnification for losses incurred by customers.
While there are countless industry-specific metrics defined by the different services provided, many of them fall under larger umbrella categories. To be successful, it is important for business teams and IT service management teams to work together to improve service delivery and meet customer expectations.
Benefits of monitoring SLA metrics
Monitoring SLA metrics is the most efficient way for enterprises to gauge whether IT services are meeting customer expectations and to pinpoint areas for improvement. By monitoring metrics and KPIs in real time, IT teams can identify system weaknesses and optimize service delivery.
The main benefits of monitoring SLA metrics include:
Greater observability
A clear end-to-end understanding of business operations helps ITSM teams find ways to improve performance. Greater observability enables organizations to gain insights into the operation of systems and workflows, identify errors, balance workloads more efficiently and improve performance standards.
Optimized performance
By monitoring the right metrics and using the insights gleaned from them, organizations can provide better services and applications, exceed customer expectations and drive business growth.
Increased customer satisfaction
Similarly, monitoring SLA metrics and KPIs is one of the best ways to make sure services are meeting customer needs. In a crowded business field, customer satisfaction is a key factor in driving customer retention and building a positive reputation.
Greater transparency
By clearly outlining the terms of service, SLAs help eliminate confusion and protect all parties. Well-crafted SLAs make it clear what all stakeholders can expect, offer a well-defined timeline of when services will be provided and which stakeholders are responsible for specific actions. When done right, SLAs help set the tone for a smooth partnership.
Understand performance and exceed customer expectations
The IBM® Instana® Observability platform and IBM Cloud Pak® for AIOps can help teams get stronger insights from their data and improve service delivery.
IBM® Instana® Observability offers full-stack observability in real time, combining automation, context and intelligent action into one platform. Instana helps break down operational silos and provides access to data across DevOps, SRE, platform engineering and ITOps teams.
IT service management teams benefit from IBM Cloud Pak for AIOps through automated tools that address incident management and remediation. IBM Cloud Pak for AIOps offers tools for innovation and the transformation if IT operations. Meet SLAs and monitor metrics with an advanced visibility solution that offers context into dependencies across environments.
IBM Cloud Pak for AIOps is an AIOps platform that delivers visibility into performance data and dependencies across environments. It enables ITOps managers and site reliability engineers (SREs) to use artificial intelligence, machine learning and automation to better address incident management and remediation. With IBM Cloud Pak for AIOps, teams can innovate faster, reduce operational cost and transform IT operations (ITOps).
Explore IBM Instana Observability
Explore IBM Cloud Pak for AIOps
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